Automated Aortic Quantification Based on Artificial Intelligence: Validation Using Contrast-Enhanced and Non-Contrast CT Scans from the Same Session

Artificial intelligence (AI) advancements have enabled automated aortic quantification in computed tomography (CT) imaging. We aimed to develop an AI method for quantifying the aorta in both contrast-enhanced and non-contrast CT scans, assisting early detection of aortic dilation. A total of 190 patient cases were analyzed, each having paired con-trast-enhanced and non-contrast CT scans acquired in the same session, resulting in 380 scans. Our approach, based on open-source tools, demonstrated strong agreement with manual annotations, particularly in the ascending aorta. For contrast-enhanced CT, the AI achieved a correlation coefficient of 0.987 and intraclass correlation coefficient (ICC) of 0.986; for non-contrast CT, both were 0.945. Compared with clinical records, the sensi-tivity of AI detection was 97% for contrast-enhanced CT and 94% for non-contrast CT. This AI-based approach offers highly sensitive, automated aortic quantification across contrast conditions, supporting physicians in early identification of aortic abnormalities.